NDLI: Kernelized type-2 fuzzy c-means clustering algorithm in segmentation of noisy medical images

Content Provider	IEEE Xplore Digital Library
Author	Kaur, P. Lamba, I.M.S. Gosain, A.
Copyright Year	2011
Description	Author affiliation: Deptt. of Information Tech., MSIT, Affiliated to GGSIP University, New Delhi, INDIA (Kaur, P.) \|\| Deptt. of Computer Sci., SET, Sharda University, Greater Noida, Uttar Pradesh, INDIA (Lamba, I.M.S.) \|\| USIT, GGSIP University, New Delhi, INDIA (Gosain, A.)
Abstract	The toughest challenges in medical diagnosis are uncertainty handling and noise. This paper presents a novel kernelized type-2 fuzzy c-means algorithm that is a generalization of conventional type-2 fuzzy c-means (T2FCM). Although T2FCM has proven effective for spherical data, it fails when the data structure of input patterns is non-spherical and complex. In this paper, we present a novel kernelized type-2 fuzzy c-means (KT2FCM) where type-2 fuzzy c-means is extended by adopting a kernel induced metric in the data space to replace the original Euclidean norm metric. Use of kernel function makes it possible to cluster data that is linearly non-separable in the original space into homogeneous groups in the transformed high dimensional space. From our experiments, we found that different kernel with different kernel widths lead to different clustering results. Thus a key point is to choose an appropriate value for the kernel width. Experimental are done using synthetic and real medical images (CT Scan and MR images) to show the effectiveness of method.
Starting Page	493
Ending Page	498
File Size	913953
Page Count	6
File Format	PDF
ISBN	9781424494781
e-ISBN	9781424494774
DOI	10.1109/RAICS.2011.6069361
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-22
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Fuzzy Clustering Type-2 Fuzzy C-Means Noise Clustering algorithms Kernal methods Noise measurement Kernel Biomedical imaging Robust Image Segmentation Kernel based Type-2 fuzzy c-means
Content Type	Text
Resource Type	Article

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